{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Selection Based on Mutual Information (Entropy) Gain for Classification and Regression "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Watch it here: https://youtu.be/GpL_XtRVne4\n",
    "#### Watch Full Playlist: https://www.youtube.com/playlist?list=PLc2rvfiptPSQYzmDIFuq2PqN2n28ZjxDH"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is Mutual Information "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The elimination process aims to reduce the size of the input feature set and at the same time to retain the class discriminatory information for classification problems."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mutual information (MI) is a measure of the amount of information between two random variables is symmetric and non-negative, and it could be zero if and only if the variables are independent. "
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is NP hard optimization problem in computer science branch. The best approach which we in general follow is greedy solution for feature selection. Those approaches are step-wise forward feature selection or step-wise backward feature selection. "
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Classification Problem\n",
    "Dataset Available at: https://github.com/laxmimerit/Data-Files-for-Feature-Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import VarianceThreshold, mutual_info_classif, mutual_info_regression\n",
    "from sklearn.feature_selection import SelectKBest, SelectPercentile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>var3</th>\n",
       "      <th>var15</th>\n",
       "      <th>imp_ent_var16_ult1</th>\n",
       "      <th>imp_op_var39_comer_ult1</th>\n",
       "      <th>imp_op_var39_comer_ult3</th>\n",
       "      <th>imp_op_var40_comer_ult1</th>\n",
       "      <th>imp_op_var40_comer_ult3</th>\n",
       "      <th>imp_op_var40_efect_ult1</th>\n",
       "      <th>imp_op_var40_efect_ult3</th>\n",
       "      <th>...</th>\n",
       "      <th>saldo_medio_var33_hace2</th>\n",
       "      <th>saldo_medio_var33_hace3</th>\n",
       "      <th>saldo_medio_var33_ult1</th>\n",
       "      <th>saldo_medio_var33_ult3</th>\n",
       "      <th>saldo_medio_var44_hace2</th>\n",
       "      <th>saldo_medio_var44_hace3</th>\n",
       "      <th>saldo_medio_var44_ult1</th>\n",
       "      <th>saldo_medio_var44_ult3</th>\n",
       "      <th>var38</th>\n",
       "      <th>TARGET</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>39205.170000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>34</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49278.030000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67333.770000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>37</td>\n",
       "      <td>0.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>64007.970000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>39</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>117310.979016</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 371 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID  var3  var15  imp_ent_var16_ult1  imp_op_var39_comer_ult1  \\\n",
       "0   1     2     23                 0.0                      0.0   \n",
       "1   3     2     34                 0.0                      0.0   \n",
       "2   4     2     23                 0.0                      0.0   \n",
       "3   8     2     37                 0.0                    195.0   \n",
       "4  10     2     39                 0.0                      0.0   \n",
       "\n",
       "   imp_op_var39_comer_ult3  imp_op_var40_comer_ult1  imp_op_var40_comer_ult3  \\\n",
       "0                      0.0                      0.0                      0.0   \n",
       "1                      0.0                      0.0                      0.0   \n",
       "2                      0.0                      0.0                      0.0   \n",
       "3                    195.0                      0.0                      0.0   \n",
       "4                      0.0                      0.0                      0.0   \n",
       "\n",
       "   imp_op_var40_efect_ult1  imp_op_var40_efect_ult3  ...  \\\n",
       "0                        0                        0  ...   \n",
       "1                        0                        0  ...   \n",
       "2                        0                        0  ...   \n",
       "3                        0                        0  ...   \n",
       "4                        0                        0  ...   \n",
       "\n",
       "   saldo_medio_var33_hace2  saldo_medio_var33_hace3  saldo_medio_var33_ult1  \\\n",
       "0                      0.0                      0.0                     0.0   \n",
       "1                      0.0                      0.0                     0.0   \n",
       "2                      0.0                      0.0                     0.0   \n",
       "3                      0.0                      0.0                     0.0   \n",
       "4                      0.0                      0.0                     0.0   \n",
       "\n",
       "   saldo_medio_var33_ult3  saldo_medio_var44_hace2  saldo_medio_var44_hace3  \\\n",
       "0                     0.0                      0.0                      0.0   \n",
       "1                     0.0                      0.0                      0.0   \n",
       "2                     0.0                      0.0                      0.0   \n",
       "3                     0.0                      0.0                      0.0   \n",
       "4                     0.0                      0.0                      0.0   \n",
       "\n",
       "   saldo_medio_var44_ult1  saldo_medio_var44_ult3          var38  TARGET  \n",
       "0                     0.0                     0.0   39205.170000       0  \n",
       "1                     0.0                     0.0   49278.030000       0  \n",
       "2                     0.0                     0.0   67333.770000       0  \n",
       "3                     0.0                     0.0   64007.970000       0  \n",
       "4                     0.0                     0.0  117310.979016       0  \n",
       "\n",
       "[5 rows x 371 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('santander.csv', nrows = 20000)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((20000, 370), (20000,))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = data.drop('TARGET', axis = 1)\n",
    "y = data['TARGET']\n",
    "\n",
    "X.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0, stratify = y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove constant, quasi constant, and duplicate features "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "constant_filter = VarianceThreshold(threshold=0.01)\n",
    "constant_filter.fit(X_train)\n",
    "X_train_filter = constant_filter.transform(X_train)\n",
    "X_test_filter = constant_filter.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_T = X_train_filter.T\n",
    "X_test_T = X_test_filter.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_T = pd.DataFrame(X_train_T)\n",
    "X_test_T = pd.DataFrame(X_test_T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_T.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "duplicated_features = X_train_T.duplicated()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_to_keep = [not index for index in duplicated_features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_unique = X_train_T[features_to_keep].T\n",
    "X_test_unique = X_test_T[features_to_keep].T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((16000, 227), (4000, 227))"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_unique.shape, X_test_unique.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate the MI "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "mi = mutual_info_classif(X_train_unique, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "227"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(mi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.68003208e-03, 2.34042929e-03, 1.15087441e-02, 0.00000000e+00,\n",
       "       2.86918620e-04, 2.78306351e-03, 2.32310495e-03, 1.21676256e-03,\n",
       "       0.00000000e+00, 5.58464658e-04, 0.00000000e+00, 0.00000000e+00,\n",
       "       1.37537844e-03, 0.00000000e+00, 1.03578561e-03, 0.00000000e+00,\n",
       "       5.86610296e-04, 1.12446180e-03, 0.00000000e+00, 1.20041192e-03,\n",
       "       7.61191860e-04, 3.74014917e-03, 1.09857326e-02, 0.00000000e+00,\n",
       "       0.00000000e+00, 8.11951690e-04, 2.57920620e-03, 2.38551976e-03,\n",
       "       1.76414654e-03, 0.00000000e+00, 1.50903586e-03, 2.47960900e-03,\n",
       "       0.00000000e+00, 3.47573294e-04, 2.18302853e-03, 0.00000000e+00,\n",
       "       5.04068209e-04, 4.33083917e-03, 1.29410049e-02, 0.00000000e+00,\n",
       "       0.00000000e+00, 3.72385417e-03, 4.17224927e-03, 1.40639203e-03,\n",
       "       3.15111906e-03, 1.04621088e-02, 4.73148088e-03, 6.88024127e-03,\n",
       "       0.00000000e+00, 1.62220502e-03, 1.60385470e-03, 4.04120909e-04,\n",
       "       1.68315841e-03, 1.72029101e-03, 2.61153119e-03, 3.27901726e-03,\n",
       "       1.76970238e-03, 1.71931452e-03, 1.28039120e-03, 1.84632933e-03,\n",
       "       0.00000000e+00, 0.00000000e+00, 1.50000613e-03, 8.94460246e-04,\n",
       "       0.00000000e+00, 1.77128330e-03, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 1.33903949e-03, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 1.05227642e-03, 3.04877132e-04, 2.79718491e-03,\n",
       "       7.64772028e-03, 1.56177886e-03, 0.00000000e+00, 1.52034705e-03,\n",
       "       8.31964541e-03, 1.29684775e-04, 0.00000000e+00, 6.00006038e-03,\n",
       "       3.86762967e-03, 1.61231690e-03, 7.51753815e-03, 0.00000000e+00,\n",
       "       9.51148543e-04, 3.43354678e-03, 8.68933811e-03, 0.00000000e+00,\n",
       "       7.75500001e-04, 0.00000000e+00, 0.00000000e+00, 1.50240667e-03,\n",
       "       3.26308208e-03, 0.00000000e+00, 1.03462105e-04, 7.83111670e-04,\n",
       "       0.00000000e+00, 2.22584062e-03, 0.00000000e+00, 1.03171205e-02,\n",
       "       1.84100238e-03, 1.68743760e-03, 0.00000000e+00, 0.00000000e+00,\n",
       "       1.01999683e-02, 0.00000000e+00, 5.77856216e-03, 9.19798974e-04,\n",
       "       2.65758087e-03, 0.00000000e+00, 0.00000000e+00, 3.27595371e-03,\n",
       "       2.02544197e-03, 1.77547299e-04, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 3.03254857e-04, 0.00000000e+00, 4.11398506e-04,\n",
       "       5.12613627e-04, 4.91010431e-04, 0.00000000e+00, 0.00000000e+00,\n",
       "       4.51741642e-04, 1.61513434e-04, 1.42243311e-04, 1.85646933e-04,\n",
       "       6.71672624e-04, 0.00000000e+00, 2.87890608e-03, 0.00000000e+00,\n",
       "       2.63178810e-04, 1.77186587e-03, 0.00000000e+00, 0.00000000e+00,\n",
       "       2.35248436e-03, 0.00000000e+00, 1.60721776e-03, 1.09293982e-04,\n",
       "       4.35486207e-04, 3.77163906e-04, 0.00000000e+00, 0.00000000e+00,\n",
       "       1.60747502e-03, 1.10587133e-03, 1.22227274e-03, 1.87302495e-04,\n",
       "       6.36658347e-04, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       3.78335170e-04, 1.60367504e-04, 4.18064509e-03, 2.67333961e-04,\n",
       "       1.14709995e-02, 1.21249497e-03, 8.56097188e-04, 3.40363082e-03,\n",
       "       0.00000000e+00, 4.67761802e-03, 3.26430522e-04, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       2.91452767e-04, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 1.00136633e-03, 4.53933029e-04, 0.00000000e+00,\n",
       "       1.70092269e-03, 2.14508949e-04, 3.99076046e-05, 1.84778663e-03,\n",
       "       0.00000000e+00, 1.69429622e-03, 1.51163472e-03, 8.16073354e-03,\n",
       "       8.27982683e-03, 7.70810505e-03, 7.67697981e-03, 5.72886759e-04,\n",
       "       0.00000000e+00, 8.24266902e-04, 2.31300837e-03, 1.31759136e-03,\n",
       "       5.67472419e-04, 1.27291946e-03, 2.00642873e-03, 3.05139797e-03,\n",
       "       0.00000000e+00, 2.40616187e-03, 1.19763109e-04, 5.05565877e-04,\n",
       "       1.05370306e-03, 4.54295807e-04, 0.00000000e+00, 1.41744399e-03,\n",
       "       1.75938218e-03, 3.40878336e-03, 7.78537206e-04, 0.00000000e+00,\n",
       "       2.71877995e-03, 0.00000000e+00, 1.91237809e-03, 3.69224485e-03,\n",
       "       3.84577935e-04, 0.00000000e+00, 1.25865692e-03, 3.34171990e-03,\n",
       "       2.21416823e-04, 0.00000000e+00, 1.90477977e-03])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "mi = pd.Series(mi)\n",
    "mi.index = X_train_unique.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "mi.sort_values(ascending=False, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e9760b5e80>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mi.plot.bar(figsize = (16,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([  2,  22,  26,  30,  40,  46,  49,  51,  86,  91, 101, 105, 119,\n",
       "            125, 127, 160, 168, 182, 209, 210, 211, 212, 229],\n",
       "           dtype='int64')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sel = SelectPercentile(mutual_info_classif, percentile=10).fit(X_train_unique, y_train)\n",
    "X_train_unique.columns[sel.get_support()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train_unique.columns[sel.get_support()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on SelectPercentile in module sklearn.feature_selection.univariate_selection object:\n",
      "\n",
      "class SelectPercentile(_BaseFilter)\n",
      " |  SelectPercentile(score_func=<function f_classif at 0x000001E964B30268>, percentile=10)\n",
      " |  \n",
      " |  Select features according to a percentile of the highest scores.\n",
      " |  \n",
      " |  Read more in the :ref:`User Guide <univariate_feature_selection>`.\n",
      " |  \n",
      " |  Parameters\n",
      " |  ----------\n",
      " |  score_func : callable\n",
      " |      Function taking two arrays X and y, and returning a pair of arrays\n",
      " |      (scores, pvalues) or a single array with scores.\n",
      " |      Default is f_classif (see below \"See also\"). The default function only\n",
      " |      works with classification tasks.\n",
      " |  \n",
      " |  percentile : int, optional, default=10\n",
      " |      Percent of features to keep.\n",
      " |  \n",
      " |  Attributes\n",
      " |  ----------\n",
      " |  scores_ : array-like, shape=(n_features,)\n",
      " |      Scores of features.\n",
      " |  \n",
      " |  pvalues_ : array-like, shape=(n_features,)\n",
      " |      p-values of feature scores, None if `score_func` returned only scores.\n",
      " |  \n",
      " |  Examples\n",
      " |  --------\n",
      " |  >>> from sklearn.datasets import load_digits\n",
      " |  >>> from sklearn.feature_selection import SelectPercentile, chi2\n",
      " |  >>> X, y = load_digits(return_X_y=True)\n",
      " |  >>> X.shape\n",
      " |  (1797, 64)\n",
      " |  >>> X_new = SelectPercentile(chi2, percentile=10).fit_transform(X, y)\n",
      " |  >>> X_new.shape\n",
      " |  (1797, 7)\n",
      " |  \n",
      " |  Notes\n",
      " |  -----\n",
      " |  Ties between features with equal scores will be broken in an unspecified\n",
      " |  way.\n",
      " |  \n",
      " |  See also\n",
      " |  --------\n",
      " |  f_classif: ANOVA F-value between label/feature for classification tasks.\n",
      " |  mutual_info_classif: Mutual information for a discrete target.\n",
      " |  chi2: Chi-squared stats of non-negative features for classification tasks.\n",
      " |  f_regression: F-value between label/feature for regression tasks.\n",
      " |  mutual_info_regression: Mutual information for a continuous target.\n",
      " |  SelectKBest: Select features based on the k highest scores.\n",
      " |  SelectFpr: Select features based on a false positive rate test.\n",
      " |  SelectFdr: Select features based on an estimated false discovery rate.\n",
      " |  SelectFwe: Select features based on family-wise error rate.\n",
      " |  GenericUnivariateSelect: Univariate feature selector with configurable mode.\n",
      " |  \n",
      " |  Method resolution order:\n",
      " |      SelectPercentile\n",
      " |      _BaseFilter\n",
      " |      sklearn.base.BaseEstimator\n",
      " |      sklearn.feature_selection.base.SelectorMixin\n",
      " |      abc.NewBase\n",
      " |      sklearn.base.TransformerMixin\n",
      " |      builtins.object\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __init__(self, score_func=<function f_classif at 0x000001E964B30268>, percentile=10)\n",
      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data and other attributes defined here:\n",
      " |  \n",
      " |  __abstractmethods__ = frozenset()\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from _BaseFilter:\n",
      " |  \n",
      " |  fit(self, X, y)\n",
      " |      Run score function on (X, y) and get the appropriate features.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : array-like, shape = [n_samples, n_features]\n",
      " |          The training input samples.\n",
      " |      \n",
      " |      y : array-like, shape = [n_samples]\n",
      " |          The target values (class labels in classification, real numbers in\n",
      " |          regression).\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      self : object\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from sklearn.base.BaseEstimator:\n",
      " |  \n",
      " |  __getstate__(self)\n",
      " |  \n",
      " |  __repr__(self)\n",
      " |      Return repr(self).\n",
      " |  \n",
      " |  __setstate__(self, state)\n",
      " |  \n",
      " |  get_params(self, deep=True)\n",
      " |      Get parameters for this estimator.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      deep : boolean, optional\n",
      " |          If True, will return the parameters for this estimator and\n",
      " |          contained subobjects that are estimators.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      params : mapping of string to any\n",
      " |          Parameter names mapped to their values.\n",
      " |  \n",
      " |  set_params(self, **params)\n",
      " |      Set the parameters of this estimator.\n",
      " |      \n",
      " |      The method works on simple estimators as well as on nested objects\n",
      " |      (such as pipelines). The latter have parameters of the form\n",
      " |      ``<component>__<parameter>`` so that it's possible to update each\n",
      " |      component of a nested object.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      self\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors inherited from sklearn.base.BaseEstimator:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from sklearn.feature_selection.base.SelectorMixin:\n",
      " |  \n",
      " |  get_support(self, indices=False)\n",
      " |      Get a mask, or integer index, of the features selected\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      indices : boolean (default False)\n",
      " |          If True, the return value will be an array of integers, rather\n",
      " |          than a boolean mask.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      support : array\n",
      " |          An index that selects the retained features from a feature vector.\n",
      " |          If `indices` is False, this is a boolean array of shape\n",
      " |          [# input features], in which an element is True iff its\n",
      " |          corresponding feature is selected for retention. If `indices` is\n",
      " |          True, this is an integer array of shape [# output features] whose\n",
      " |          values are indices into the input feature vector.\n",
      " |  \n",
      " |  inverse_transform(self, X)\n",
      " |      Reverse the transformation operation\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : array of shape [n_samples, n_selected_features]\n",
      " |          The input samples.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      X_r : array of shape [n_samples, n_original_features]\n",
      " |          `X` with columns of zeros inserted where features would have\n",
      " |          been removed by `transform`.\n",
      " |  \n",
      " |  transform(self, X)\n",
      " |      Reduce X to the selected features.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : array of shape [n_samples, n_features]\n",
      " |          The input samples.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      X_r : array of shape [n_samples, n_selected_features]\n",
      " |          The input samples with only the selected features.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from sklearn.base.TransformerMixin:\n",
      " |  \n",
      " |  fit_transform(self, X, y=None, **fit_params)\n",
      " |      Fit to data, then transform it.\n",
      " |      \n",
      " |      Fits transformer to X and y with optional parameters fit_params\n",
      " |      and returns a transformed version of X.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : numpy array of shape [n_samples, n_features]\n",
      " |          Training set.\n",
      " |      \n",
      " |      y : numpy array of shape [n_samples]\n",
      " |          Target values.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      X_new : numpy array of shape [n_samples, n_features_new]\n",
      " |          Transformed array.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(sel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_mi = sel.transform(X_train_unique)\n",
    "X_test_mi = sel.transform(X_test_unique)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16000, 23)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_mi.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build the model and compare the performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_randomForest(X_train, X_test, y_train, y_test):\n",
    "    clf = RandomForestClassifier(n_estimators=100, random_state=0, n_jobs=-1)\n",
    "    clf.fit(X_train, y_train)\n",
    "    y_pred = clf.predict(X_test)\n",
    "    print('Accuracy on test set: ')\n",
    "    print(accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on test set: \n",
      "0.958\n",
      "Wall time: 571 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "run_randomForest(X_train_mi, X_test_mi, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on test set: \n",
      "0.9585\n",
      "Wall time: 1.46 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "run_randomForest(X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60.95890410958904"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(1.46-0.57)*100/1.46\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mutual Information Gain in Regression "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_boston\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "boston = load_boston()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _boston_dataset:\n",
      "\n",
      "Boston house prices dataset\n",
      "---------------------------\n",
      "\n",
      "**Data Set Characteristics:**  \n",
      "\n",
      "    :Number of Instances: 506 \n",
      "\n",
      "    :Number of Attributes: 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target.\n",
      "\n",
      "    :Attribute Information (in order):\n",
      "        - CRIM     per capita crime rate by town\n",
      "        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.\n",
      "        - INDUS    proportion of non-retail business acres per town\n",
      "        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n",
      "        - NOX      nitric oxides concentration (parts per 10 million)\n",
      "        - RM       average number of rooms per dwelling\n",
      "        - AGE      proportion of owner-occupied units built prior to 1940\n",
      "        - DIS      weighted distances to five Boston employment centres\n",
      "        - RAD      index of accessibility to radial highways\n",
      "        - TAX      full-value property-tax rate per $10,000\n",
      "        - PTRATIO  pupil-teacher ratio by town\n",
      "        - B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n",
      "        - LSTAT    % lower status of the population\n",
      "        - MEDV     Median value of owner-occupied homes in $1000's\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "\n",
      "    :Creator: Harrison, D. and Rubinfeld, D.L.\n",
      "\n",
      "This is a copy of UCI ML housing dataset.\n",
      "https://archive.ics.uci.edu/ml/machine-learning-databases/housing/\n",
      "\n",
      "\n",
      "This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\n",
      "\n",
      "The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\n",
      "prices and the demand for clean air', J. Environ. Economics & Management,\n",
      "vol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\n",
      "...', Wiley, 1980.   N.B. Various transformations are used in the table on\n",
      "pages 244-261 of the latter.\n",
      "\n",
      "The Boston house-price data has been used in many machine learning papers that address regression\n",
      "problems.   \n",
      "     \n",
      ".. topic:: References\n",
      "\n",
      "   - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\n",
      "   - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(boston.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
       "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
       "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
       "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
       "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
       "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
       "\n",
       "   PTRATIO       B  LSTAT  \n",
       "0     15.3  396.90   4.98  \n",
       "1     17.8  396.90   9.14  \n",
       "2     17.8  392.83   4.03  \n",
       "3     18.7  394.63   2.94  \n",
       "4     18.7  396.90   5.33  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = pd.DataFrame(data = boston.data, columns=boston.feature_names)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = boston.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "mi = mutual_info_regression(X_train, y_train)\n",
    "mi = pd.Series(mi)\n",
    "mi.index = X_train.columns\n",
    "mi.sort_values(ascending=False, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LSTAT      0.679664\n",
       "RM         0.560279\n",
       "INDUS      0.514883\n",
       "PTRATIO    0.476028\n",
       "NOX        0.453753\n",
       "TAX        0.388035\n",
       "CRIM       0.355854\n",
       "AGE        0.342615\n",
       "DIS        0.319469\n",
       "ZN         0.202716\n",
       "RAD        0.188252\n",
       "B          0.147638\n",
       "CHAS       0.028292\n",
       "dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e975dd50b8>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mi.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CRIM', 'INDUS', 'NOX', 'RM', 'AGE', 'DIS', 'TAX', 'PTRATIO', 'LSTAT'], dtype='object')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sel = SelectKBest(mutual_info_regression, k = 9).fit(X_train, y_train)\n",
    "X_train.columns[sel.get_support()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LinearRegression()\n",
    "model.fit(X_train, y_train)\n",
    "y_predict = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5892223849182492"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test, y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.783509315085146"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(mean_squared_error(y_test, y_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.188011545278203"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(404, 9)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_9 = sel.transform(X_train)\n",
    "X_train_9.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_9 = sel.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_score\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.5317127606961576"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "model.fit(X_train_9, y_train)\n",
    "y_predict = model.predict(X_test_9)\n",
    "print('r2_score')\n",
    "r2_score(y_test, y_predict)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rmse\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "6.175103151293747"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('rmse')\n",
    "np.sqrt(mean_squared_error(y_test, y_predict))"
   ]
  }
 ],
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